IEEE transactions on visualization and computer graphics最新文献_第7页

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation. 为什么我看不到我的集群？降维验证的精确查全率方法。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-24 DOI: 10.1109/TVCG.2026.3726607

Diede P M van der Hoorn, Alessio Arleo, Fernando V Paulovich

{"title":"Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation.","authors":"Diede P M van der Hoorn, Alessio Arleo, Fernando V Paulovich","doi":"10.1109/TVCG.2026.3726607","DOIUrl":"10.1109/TVCG.2026.3726607","url":null,"abstract":"Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. However, such a structure may not always appear in the projections. Existing DR quality metrics assess projection reliability (to some extent) or cluster structure quality, but do not explain why expected structures are missing. Visual Analytics solutions can help, but are often time-consuming due to the large hyperparameter space. This paper addresses this problem by leveraging a recent framework that divides the DR process into two phases: a relationship phase, where similarity relationships are modeled, and a mapping phase, where the data is projected accordingly. We introduce two supervised metrics, precision and recall, to evaluate the relationship phase. These metrics quantify how well the modeled relationships align with an expected cluster structure based on some set of labels representing this structure. We illustrate their application using t-SNE and UMAP, and validate the approach through various usage scenarios. Our approach can guide hyperparameter tuning, uncover projection artifacts, and determine if the expected structure is captured in the relationships, making the DR process faster and more reliable.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpreting FMS and SSQ Cybersickness Ratings via User Tolerance in Virtual Reality. 通过虚拟现实中的用户容忍度解释FMS和SSQ晕动症评分。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-24 DOI: 10.1109/TVCG.2026.3726605

Jonathan W Kelly, Michael C Dorneich, Stephen B Gilbert

{"title":"Interpreting FMS and SSQ Cybersickness Ratings via User Tolerance in Virtual Reality.","authors":"Jonathan W Kelly, Michael C Dorneich, Stephen B Gilbert","doi":"10.1109/TVCG.2026.3726605","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3726605","url":null,"abstract":"Cybersickness remains a persistent challenge limiting the usability of virtual reality (VR), yet commonly used subjective measures such as the Simulator Sickness Questionnaire (SSQ) and the Fast Motion Sickness (FMS) scale are often difficult to interpret in terms of user outcomes. In particular, there is limited guidance on how specific values on these scales relate to user tolerance or the likelihood that users will discontinue a VR experience due to discomfort. This work addresses this gap by calibrating cybersickness severity interpretations against early termination of a VR experience. In Study 1 (N = 183), participants played an immersive VR game for up to 20 minutes and were free to terminate the experience at any time. Using a Behavioral Risk Banding (BRB) framework, logistic regression linked a modified 0-10 version of the FMS (FMS-10) and post-exposure SSQ scores to dropout probability, yielding behaviorally grounded risk severity bands (mild, moderate, severe, extreme). Survival analyses then showed how these bands mapped onto tolerance over time, revealing a transitional region marked by heightened individual variability. Study 2 (N = 304) evaluated the robustness of these empirically derived severity categories across variation in VR content and mitigation context by applying the Study 1 thresholds without re-estimation. Although absolute dropout risk varied, the ordering and behavioral meaning of severity categories were preserved. Together, these results provide a practical, behaviorally grounded framework and empirically derived thresholds for interpreting SSQ and FMS cybersickness scores in terms of user tolerance and VR usability across contexts.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instant Preference Alignment for Text-to-Image Diffusion Models. 文本到图像扩散模型的即时偏好对齐。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-21 DOI: 10.1109/TVCG.2026.3726123

Yang Li, Songlin Yang, Xiaoxuan Han, Wei Wang, Jing Dong, Yueming Lyu, Ziyu Xue

{"title":"Instant Preference Alignment for Text-to-Image Diffusion Models.","authors":"Yang Li, Songlin Yang, Xiaoxuan Han, Wei Wang, Jing Dong, Yueming Lyu, Ziyu Xue","doi":"10.1109/TVCG.2026.3726123","DOIUrl":"10.1109/TVCG.2026.3726123","url":null,"abstract":"Text-to-image (T2I) generation has greatly enhanced creative expression, yet achieving preference-aligned generation in a real-time and training-free manner remains challenging. Previous methods often rely on static, pre-collected preferences or fine-tuning, limiting adaptability to evolving and nuanced user intents. In this paper, we highlight the need for instant preference-aligned T2I generation and propose a training-free framework grounded in multimodal large language model (MLLM) priors. Our framework decouples the task into two components: preference understanding and preferenceguided generation. For preference understanding, we leverage MLLMs to automatically extract global preference signals from a reference image and enrich a given prompt using structured instruction design. Our approach supports broader and more fine-grained coverage of user preferences than existing methods. For preference-guided generation, we integrate global keywordbased control and local region-aware cross-attention modulation to steer the diffusion model without additional training, enabling precise alignment across both global attributes and local elements. The entire framework supports multi-round interactive refinement, facilitating real-time and context-aware image generation. Extensive experiments on the Viper dataset and our collected benchmark demonstrate that our method outperforms prior approaches in both quantitative metrics and human evaluations, and opens up new possibilities for dialogbased generation and MLLM-diffusion integration.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

InfoLoD: Training-Data-Free Hierarchical 3D Gaussian Splatting via Fisher-Guided View Synthesis. InfoLoD：通过fisher引导视图合成的无训练数据分层三维高斯飞溅。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-20 DOI: 10.1109/TVCG.2026.3725300

Zhenyu Xia, Pengcheng Han, Lin Chen, Shuhui Bu

{"title":"InfoLoD: Training-Data-Free Hierarchical 3D Gaussian Splatting via Fisher-Guided View Synthesis.","authors":"Zhenyu Xia, Pengcheng Han, Lin Chen, Shuhui Bu","doi":"10.1109/TVCG.2026.3725300","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3725300","url":null,"abstract":"Real-time visualization of massive 3D virtual environments, such as city-scale digital twins and immersive simulations, remains a core challenge in computer graphics. While 3D Gaussian Splatting (3DGS) achieves photorealistic rendering at interactive rates, its explicit Gaussian representation incurs substantial GPU memory overhead, limiting deployment on resource-constrained devices. Existing 3DGS Level-of-Detail (LoD) methods are often data-dependent, requiring access to large collections of original training images, and they rarely adapt to dynamic hardware budgets, leading to unstable performance on low-VRAM GPUs. We present InfoLoD, a data-free and hardware-aware framework for hierarchical LoD construction and large-scale 3DGS rendering. InfoLoD introduces a Fisher-guided self-distillation scheme that uses the Fisher Information Matrix (FIM) to select geometrically valid, information-rich pseudo viewpoints, enabling LoD training directly from a pre-trained 3DGS model without any original images. To operate under strict VRAM constraints, we further propose a budget-aware out-of-core streaming system with spatio-temporal working-set management and resource-adaptive LoD scheduling to balance fidelity and throughput. Experiments on challenging outdoor scenes show that InfoLoD enables stable real-time rendering on resource-constrained GPUs, delivering higher efficiency than prior baselines while maintaining competitive visual quality.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TopoColor: Topology-Aware Region Correspondence for Line Art Colorization. TopoColor：线条艺术着色的拓扑感知区域对应。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-20 DOI: 10.1109/TVCG.2026.3725399

Bin Huang, Haoran Mo, Chengying Gao

{"title":"TopoColor: Topology-Aware Region Correspondence for Line Art Colorization.","authors":"Bin Huang, Haoran Mo, Chengying Gao","doi":"10.1109/TVCG.2026.3725399","DOIUrl":"10.1109/TVCG.2026.3725399","url":null,"abstract":"Line art colorization is a critical yet labor-intensive bottleneck in professional animation production, requiring artists to maintain rigorous color consistency across frames. While contemporary methods leveraging feature alignment or generative diffusion have improved efficiency, they often fail when confronted with occlusions and large inter-frame deformations. The fundamental difficulty arises from the inherent sparsity of binary line-art images: the absence of internal texture limits the discriminative power of local feature descriptors, resulting in ambiguous region correspondences during propagation. To address these limitations, we propose a region-based feature enhancement framework built upon a Topology-aware Segment Graph (TSG). By explicitly modeling topological adjacency-a robust structural cue that remains invariant across frames despite significant geometric changes-our method leverages spatial context to increase feature separability across semantically distinct regions, enabling more robust alignment. Furthermore, to mitigate temporal drift and the diminishing relevance of static reference frames in long sequences, we introduce a Dual-source Temporal Selection and Refinement strategy. This mechanism adaptively integrates information from both the global reference keyframe and the immediate previous frame, balancing color fidelity with spatial continuity. Extensive experiments on industrial benchmarks demonstrate that our approach achieves superior color accuracy and temporal stability compared to state-of-the-art frameworks, particularly in scenarios involving complex character motion and topological variation.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model. AnyTalk：利用视频生成模型的任意角色的语音动画。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-19 DOI: 10.1109/TVCG.2026.3725254

Kwan Yun, Serin Yoon, Sunjin Jung, Jung Eun Yoo, Inyup Lee, Junyong Noh

{"title":"AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model.","authors":"Kwan Yun, Serin Yoon, Sunjin Jung, Jung Eun Yoo, Inyup Lee, Junyong Noh","doi":"10.1109/TVCG.2026.3725254","DOIUrl":"10.1109/TVCG.2026.3725254","url":null,"abstract":"We present AnyTalk, a novel method for generating 3D speech animations for arbitrary characters without requiring any animation data. While existing audio-driven 3D speech animation methods rely on character-specific training data or laborious rigging/re-meshing, AnyTalk circumvents these limitations by leveraging recent video diffusion models trained on extensive video datasets. We first adapt a pre-trained video diffusion model to a target character through our Character-specific Fine-tuning (CsF) technique. By fine-tuning on rendered images of the 3D character paired with zeroed-out audio embeddings (representing \"no motion\"), we eliminate the need for animation data while preserving the motion prior of large-scale video diffusion model. We then uplift the resulting talking-head video into a 3D speech animation by estimating blendshape parameters through a proposed optimization process. AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements. We further enhance usability by distilling AnyTalk into a streamlined network, $text{AnyTalk}_{RT}$, thereby enabling real-time performance. By leveraging talking-head video generation, our method broadens access to audio-driven speech animation technology for arbitrary characters. The code is publicly available at AnyTalk.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Predictive VR Interaction for Fine Teleoperation Using Physics-Based simulation and automatic planning. 基于物理仿真和自动规划的精细远程操作的高效预测VR交互。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-18 DOI: 10.1109/TVCG.2026.3724849

Xinhai Wu, Tiantian Wang, Xinyu Zhang

{"title":"Efficient Predictive VR Interaction for Fine Teleoperation Using Physics-Based simulation and automatic planning.","authors":"Xinhai Wu, Tiantian Wang, Xinyu Zhang","doi":"10.1109/TVCG.2026.3724849","DOIUrl":"10.1109/TVCG.2026.3724849","url":null,"abstract":"In complex and hazardous environments, robots often require human's fine teleoperation to ensure accurate decision-making and optimal manipulation. To improve teleoperation efficiency, we design and develop 3D predictive Virtual Reality (VR) interfaces with four distinct automation levels. These 3D interfaces, coupled with physics-based simulation and task-level automatic planning, assist users in controlling robots for fine manipulation. Our comparative user study evaluates the efficiency of these interaction strategies, investigating the feasibility of solely relying on simulation results for robot control. We further compare VR controller-based teleoperation with direct hand gesture manipulation, assessing efficiency and user interaction experience. Our results demonstrate that predictive interfaces and intelligent assistance significantly improve operational efficiency. VR controller-based teleoperation outperforms hand gesture interaction in terms of efficiency and user experience for fine manipulation. In particular, even novice users can effectively utilize the predictive VR interface for precise remote robot control. This work presents a novel approach of integrating predictive interaction, physics-based simulation, and task-level automatic planning for fine teleoperation.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Few-Shot Novel View Synthesis with Human-Perceptual Preferences. 基于人类感知偏好的少镜头新颖视图合成优化。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-18 DOI: 10.1109/TVCG.2026.3725082

Xiaoyu Xu, Jiebin Yan, Sheyang Tang, Zhou Wang

{"title":"Optimizing Few-Shot Novel View Synthesis with Human-Perceptual Preferences.","authors":"Xiaoyu Xu, Jiebin Yan, Sheyang Tang, Zhou Wang","doi":"10.1109/TVCG.2026.3725082","DOIUrl":"10.1109/TVCG.2026.3725082","url":null,"abstract":"Novel view synthesis methods, such as neural radiance fields and 3D Gaussian splatting, offer a promising solution for photorealistic rendering. However, they remain challenged in few-shot settings, where models tend to overfit the limited supervised views, leading to artifacts such as quality fluctuations, degradation in distant views, and geometric inconsistencies. To address these issues, we introduce Human Perceptual Preference Optimization (HuPPO), a framework that incorporates human perceptual guidance into model training. HuPPO mitigates distortions by regularizing training dynamics with perceptual preference cues, thereby reducing the reliance on extensive supervised views. Specifically, HuPPO leverages human perception to identify and select candidate novel views, and introduces a corresponding objective function that steers optimization toward perceptually preferred outcomes. In addition, a meta-learning pipeline is integrated to promote the learning of generalizable representations. The framework is flexible and can be seamlessly applied to a wide range of neural rendering models without incurring additional inference overhead. Extensive experiments and analyses demonstrate that HuPPO achieves consistent improvements over state-of-the-art baselines.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences. FMReward：调整和评估音频驱动的3D面部动画与人类的偏好。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-18 DOI: 10.1109/TVCG.2026.3725120

Sijing Wu, Yunhao Li, Zhilin Gao, Huiyu Duan, Yucheng Zhu, Guangtao Zhai, Patrick Le Callet

{"title":"FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences.","authors":"Sijing Wu, Yunhao Li, Zhilin Gao, Huiyu Duan, Yucheng Zhu, Guangtao Zhai, Patrick Le Callet","doi":"10.1109/TVCG.2026.3725120","DOIUrl":"10.1109/TVCG.2026.3725120","url":null,"abstract":"Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown promising capabilities, the training and evaluation of existing methods typically rely on ground-truth-based errors, which fall short of aligning with human preferences. To address this, we present a comprehensive framework that learns an automatic perceptual model from human preference data and leverages it to improve and evaluate the perceptual quality of audio-driven 3D facial animation. To begin with, we construct FMPair (Facial Motion Pairwise preference), the first human preference dataset for audio-driven 3D facial animation, which is built through a systematic annotation pipeline and comprises 65,574 annotated 3D facial motion pairs from 8,834 distinct in-the-wild audio clips. Based on the pairwise comparison dataset, we propose a Facial Motion Reward model, termed FMReward, which takes audio and 3D facial motion as inputs and predicts a perceptual quality score aligned with human preferences. Building upon FMReward, we further introduce Facial Motion reward Feedback Learning (FMFL), a direct fine-tuning algorithm that leverages a pretrained reward model to optimize diffusion-based audio-driven 3D facial animation models for better alignment with human preferences. Extensive experiments demonstrate the superiority of FMReward over other metrics in aligning with human preferences and the effectiveness of FMFL in improving the perceptual quality of audio-driven 3D facial animation. The dataset and codes will be released at: https://github.com/wsj-sjtu/FMReward.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Upright VR: Understanding Direct Interaction and Hand Redirection Strategies in Supine Posture. 超越直立VR：理解仰卧姿势的直接交互和手重定向策略。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-18 DOI: 10.1109/TVCG.2026.3724913

Zhiqing Wu, Jiawei Li, Shumeng Zhang, Peixuan Xiong, Zisu Li, Mingming Fan

{"title":"Beyond Upright VR: Understanding Direct Interaction and Hand Redirection Strategies in Supine Posture.","authors":"Zhiqing Wu, Jiawei Li, Shumeng Zhang, Peixuan Xiong, Zisu Li, Mingming Fan","doi":"10.1109/TVCG.2026.3724913","DOIUrl":"10.1109/TVCG.2026.3724913","url":null,"abstract":"VR research predominantly focuses on upright postures, such as sitting or standing. Recent research has begun exploring VR use while lying down, revealing numerous benefits. However, existing interaction techniques are tailored for upright postures, creating challenges when applied to a lying-down position. We conducted an experiment to understand user performance across different spatial dimensions (azimuth, inclination, and distance) during a target selection task in a lying-down posture. Our findings revealed that hand movement increases nearly linearly with target offset and that users experience noticeable fatigue and a desire to minimize physical effort. Motivated by these insights, we identified two prevalent hand redirection methods, Go-Go and RBNL, and, based on empirical performance data, developed an empirical data-driven compensation scaling (DDCS) technique that dynamically scales virtual hand movements. Evaluations indicate that both DDCS and RBNL can significantly reduce hand movement and accelerate interaction in supine VR, though at the cost of increased spatial offsets that may compromise precision; however, users generally prioritize comfort over high accuracy. We further discussed the potential reasons and future implications of adopting VR in a supine posture.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0